CN115829138B - Method for predicting ticket selling quantity in real time adjustment - Google Patents
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Abstract
The application relates to a method for predicting ticket selling quantity in real time adjustment, which comprises the following steps: input data acquisition is carried out on a plurality of data influencing ticketing; preprocessing the acquired input data; predicting the traffic flow of the person according to the processed data; predicting ticket selling quantity according to the obtained people and vehicle flow prediction result; calculating the load quantity; according to the influence of various factors in the ticketing process, a real-time adjustment scheme for predicting the ticketing quantity is selected, and the prediction method is realized by using various actual data and various algorithms to perform real-time automatic adjustment of the ticketing quantity.
Description
Technical Field
The application relates to the technical field of port and aviation passenger transport algorithms, in particular to a method for predicting ticket selling quantity in real time adjustment.
Background
The existing port and navigation passenger ticket selling mode is generally a fixed ticket selling mode, a real-time dynamic adjusting mode is not adopted, and when a certain type of vehicles are more and another type of vehicles are less, the adjustment meeting the needs of customers is difficult to carry out, so that the actual ferry loading quantity is far smaller than an actual value; the existing ticketing and loading modes are mainly carried out manually, and decisions are rarely made according to data experience, so that the time length is difficult to attach; the existing loading mode mainly depends on the expected allocation of tickets, but the allocation result is generally based on an empirical value rather than the situation of a parking lot, and is easy to cause overlong waiting time of some types of vehicles.
Disclosure of Invention
The embodiment of the application provides a method for predicting ticket selling quantity in real time, which aims to solve the problems that in the prior art, the prior ticket selling is static, ticket selling initial values are given fixed values, the calculation of the length of a lane line cannot be carried out according to the capacity of a current arrival vehicle, a loaded vehicle and a ship, and the loading scheme cannot be predicted according to the historical loading result and the current arrival vehicle condition. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The application provides a method for predicting ticket selling quantity in real time adjustment, which comprises the following steps: input data acquisition is carried out on a plurality of data influencing ticketing; preprocessing the acquired input data; predicting the traffic flow of the person according to the processed data; predicting ticket selling quantity according to the obtained people and vehicle flow prediction result; calculating the load quantity; and selecting a real-time adjustment scheme for predicting the ticket selling quantity according to the influence of various factors in the ticket selling process.
As a preferred embodiment, the data that affects ticketing is input into the data collection step, and the collected data information includes one or more of historical ticketing data, weather information and holiday information, where the historical ticketing data is data within one year.
As a preferred embodiment, smoothing the possible arrival concrete data according to the historical ticketing data; and predicting the traffic flow of the person according to the processed data by using an autoregressive moving average model.
As a preferred embodiment, the specific steps of predicting the flow of the person or the vehicle according to the processed data include: constructing an autoregressive moving average model based on the processed data; the processed data are divided into a training set and a testing set according to time sequence; training the built autoregressive moving average model by adopting a training set; and predicting the test set by using the trained autoregressive moving average model.
As a preferred embodiment, the specific steps of predicting the ticket selling amount according to the obtained predicted result of the human and vehicle flow include: according to the predicted human/vehicle flow result, predicting and calculating the ticket selling quantity: according to the actual load quantity of the ship in history, calculating the actual load quantity of the ship as the meter number of the lane lines to obtain the actual load meter number of the ship; according to the training model result, predicting the flow of people and vehicles needing to be loaded by the ship by using actual data; determining the proportion of each type according to the predicted data, distributing the number of the loadable lane lines in meters, and finally converting the number into the number of each type of vehicles; and converting the number of various vehicles as a default ticketing number result.
As a preferred embodiment, the specific steps of calculating the load quantity are as follows: collecting historical data in batches to obtain the highest historical load quantity; estimating the number of lanes loaded at the time by using the average lane line occupation length of various vehicles loaded at the time according to the highest 15% loading number; according to the obtained number of the loading lanes, calculating and obtaining the maximum practical number of each lane line in the plurality of lane lines; according to the history data, learning, and counting the proportion of various vehicles according to the current ticketing and actual port-keeping conditions; dividing the length of the lane lines according to the proportion of various vehicles, and equally dividing according to the counted length of the lane lines; after the predicted number of various vehicles is obtained, the number of various on-site vehicles is distributed in real time according to the number of the on-site vehicles, the remaining length of the shortest lane line is solved by using a bipartite answer, and the specific position of each vehicle on each lane line is obtained.
As a preferred embodiment, the real-time adjustment scheme includes: the time period is used for adjusting one or more of different ticketing quantity, recalculating the ticketing quantity every fixed time interval, selecting the time interval to recalculate the ticketing quantity, calculating the ticketing quantity according to the ticketing quantity and recalculating the ticketing quantity according to the loaded quantity.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the scheme for automatically predicting the ticket selling quantity according to the historical data and the actual data, the prediction scheme is realized by using various actual data and various algorithms; according to the loading result and ticket selling quantity forecast, real-time automatic adjustment of ticket selling quantity is carried out; automatically carrying out load calculation according to historical data and current data, wherein the load uses a simulation estimation and accurate calculation (heuristic search A); and carrying out load scheme prediction according to the historical load result and the current number of vehicles in the port.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a graph illustrating the algorithm P-value test of the present method according to an exemplary embodiment;
FIG. 2 is a diagram illustrating the present method algorithms mae and rmse, according to an example embodiment: evaluating a prediction result;
fig. 3 is a graph of stationary data column detection results, according to an example embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in, or substituted for, those of others. The scope of the embodiments herein includes the full scope of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like herein are used merely to distinguish one element from another element and do not require or imply any actual relationship or order between the elements. Indeed the first element could also be termed a second element and vice versa. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a structure, apparatus or device comprising the element. Various embodiments are described herein in a progressive manner, each embodiment focusing on differences from other embodiments, and identical and similar parts between the various embodiments are sufficient to be seen with each other.
The terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein refer to an orientation or positional relationship based on that shown in the drawings, merely for ease of description herein and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operate in a particular orientation, and thus are not to be construed as limiting the application. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanically or electrically coupled, may be in communication with each other within two elements, may be directly coupled, or may be indirectly coupled through an intermediary, as would be apparent to one of ordinary skill in the art.
Herein, unless otherwise indicated, the term "plurality" means two or more.
Herein, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an association relation describing an object, meaning that three relations may exist. For example, a and/or B, represent: a or B, or, A and B.
Embodiments of the application and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1-3, the present embodiment provides a method for predicting ticket sales quantity in real time adjustment, which includes the following steps: input data acquisition is carried out on a plurality of data influencing ticketing; preprocessing the acquired input data; predicting the traffic flow of the person according to the processed data; predicting ticket selling quantity according to the obtained people and vehicle flow prediction result; calculating the load quantity; and selecting a real-time adjustment scheme for predicting the ticket selling quantity according to the influence of various factors in the ticket selling process.
In the step of inputting the data affecting ticketing, the collected data information comprises one or more of historical ticketing data, weather information and holiday information, wherein the historical ticketing data is data in one year. According to historical ticketing data, possible arrival concrete data are subjected to smooth processing, namely, the traffic flow and the traffic flow are assumed to be continuous, according to the final loading result, in any continuous 2 ships, the traffic flow is assumed to meet poisson distribution, and the traffic flow is uniformly spread to every 5 minutes to serve as an input node.
According to the historical ticketing data, carrying out smooth processing on possible arrival concrete data; the prediction of the traffic flow of the vehicle and the person is carried out by using an autoregressive moving average model according to the processed data, and the prediction is carried out by using an autoregressive moving average model (ARMA (p, q)) according to the processed data, wherein AR represents a p-order autoregressive process and MA represents a q-order moving average process; before training the model, checking the p value through ADF to see whether the model meets stability, wherein the p value is obviously close to 0 due to pretreatment, and the p, q=1 parameters are adopted to train the model.
The specific steps for predicting the traffic flow of the person and the vehicle according to the processed data comprise: constructing an autoregressive moving average model based on the processed data; the processed data are divided into a training set and a testing set according to time sequence; training the built autoregressive moving average model by adopting a training set; and predicting the test set by using the trained autoregressive moving average model.
Before the model is built, data preprocessing needs to be carried out on the data, wherein the data comprises an external packet which needs to be imported, such as stability detection of the data, and the like: pandas (operation dataframe), statsmode.tsa.stattools (data statistics detection), datetime (datetime type converting time column to python) step: 1.
and (3) carrying out stability detection on the data, and when the p value is smaller than 0.05 and the absolute values of the three items of the Critical values are smaller than the absolute value of the Test statistics, recognizing the data as a stable data column and modeling.
Based on the processed data, a model is built for training. The processed data should only include two columns, a time column and a data column. The external package that needs to be imported: pandas, statsmodel. Api. Tsa. ARIMA (ARMA model when I parameter is set to 0), jack (save model file), sklearn. Metrics (measure model performance index, mainstream are average absolute error and root mean square error). The steps are as follows: 1.
dividing data into a training set and a testing set according to time sequence (7:3 or 8:2 is main stream division, and the former is the size of the training set); 2. building a model and training based on a training set; 3. checking training errors of the model; 4. the model is (optionally) saved. For subsequent reuse
Model prediction uses a trained model, predicts results, compares the model with a test set, and checks the performance of the model on the test set, and an external package needs to be imported: pandas, statsmode.api.tsa.ARIMA, sklearn.metrics (measure model performance metrics), matplotlib.pyplot (visualization). The steps are as follows: 1. predicting a subsequent result by using a prediction method of the model; 2. and (3) optionally drawing the predicted result and the real result of the test set, visually observing the predicted accuracy 3. Transmitting the predicted result and the real result of the test set into an error detection method, and checking the generalization error of the model.
The specific steps of ticket selling quantity prediction according to the obtained people and vehicle flow prediction result include: according to the predicted human/vehicle flow result, predicting and calculating the ticket selling quantity: according to the actual load quantity of the ship in history, calculating the actual load quantity of the ship as the meter number of the lane lines to obtain the actual load meter number of the ship; according to the training model result, predicting the flow of people and vehicles needing to be loaded by the ship by using actual data; determining the proportion of each type according to the predicted data, distributing the number of the loadable lane lines in meters, and finally converting the number into the number of each type of vehicles; and converting the number of various vehicles as a default ticketing number result.
The specific steps of the load quantity calculation are as follows: collecting historical data in batches to obtain the highest historical load quantity; estimating the number of lanes loaded at the time by using the average lane line occupation length of various vehicles loaded at the time according to the highest 15% loading number; according to the obtained number of the loading lanes, calculating and obtaining the maximum practical number of each lane line in the plurality of lane lines; according to the history data, learning, and counting the proportion of various vehicles according to the current ticketing and actual port-keeping conditions; dividing the length of the lane lines according to the proportion of various vehicles, and equally dividing according to the counted length of the lane lines; after the predicted number of various vehicles is obtained, the number of various on-site vehicles is distributed in real time according to the number of the on-site vehicles, the remaining length of the shortest lane line is solved by using a bipartite answer, and the specific position of each vehicle on each lane line is obtained.
The simulation estimation scheme is as follows: according to the remaining total lane meters and the average value of the number with more load in the historical data, calculating the average loadable number and proportion of various vehicles, directly obtaining the numerical value of various vehicles by using F (Xn) =LeftLen/MaxLen Avg (Sigma (Xn)) and then taking the numerical value as the load number.
The binary answer method is as follows: assuming that the shortest lane line length is X and is just used, we assume that a large value Y is necessarily larger than X, at the moment, a certain group of values are between 1- > Y (generally not more than 2000 m due to the limitation of a ship), and then judging whether the length given by the current lane line is X; if the effective solution can be found by using the a-algorithm, it is considered feasible to allocate the current number of vehicles and lane line meters, and the search range is changed to X- > Y, otherwise 1- > X. Since the trolley requires at least a lane line length of no more than 5 meters, it can be stopped when the distance X- > Y is no more than 5.
The search method is as follows: 5.1 enumerating each vehicle according to the approach time; 5.2, distributing the left space to the lane line length with the largest remaining space which is not distributed; 5.3, calculating the length of the remaining lane line, and if the remaining space is insufficient for placing 1 vehicle, marking the length as a waste length; 5.4 if the current waste length+the calculated remaining minimum waste length is greater than the current existing allocation result, stopping the current branch search; 5.5 calculating the remaining minimum wasted length: according to the current waste length; 5.6 search pruning: if the searched vehicle length is consistent with the length of the occupied lane line and the current state is recorded, directly stopping the subsequent searching; 5.7 search pruning: the trolley is not searched, the length of the lane line occupied by the trolley is fixedly regarded as 5 meters, the quantity search of the collection card, the overrun car and the passenger car is only carried out, and the trolley is filled into the residual space.
The real-time adjustment scheme includes: the time period is used for adjusting one or more of different ticketing quantity, recalculating the ticketing quantity every fixed time interval, selecting the time interval to recalculate the ticketing quantity, calculating the ticketing quantity according to the ticketing quantity and recalculating the ticketing quantity according to the loaded quantity.
Because the ticket selling process is affected by various factors, real-time adjustment is needed; the existing algorithm has limited computing power, and different ticketing amounts need to be adjusted according to the actual situation and the time period.
Ticket vending automatic calculating mechanism: recalculating the ticket selling quantity every fixed time interval;
time interval selection: because the prediction algorithm is used for prediction, and the loading algorithm is called for feasibility calculation, the ticketing quantity result of each ship cannot be calculated in real time, and therefore, a timing update mechanism is designed: the ship from 7 days before sailing to 1 day before sailing is automatically adjusted for 1 time every 1 hour; 1 time of adjustment is carried out every 30 minutes from 1 day before starting to 3 hours before starting; 3 hours before sailing, 1 time every 10 minutes.
Ticket selling quantity adjusting scheme: according to the number of the sold tickets, real-time adjustment is carried out: firstly, calling a ticket selling model prediction result to obtain the proportion of various tickets to be sold; according to the number of the sold tickets, converting the number of the sold tickets into the length of the lane line of the ship, and obtaining the residual ticket-selling space of the ship; finally, determining the remaining ticketing quantity by using a proportional value obtained by a ticketing quantity prediction method according to the remaining ticketing space;
load quantity adjustment scheme: according to the loaded quantity, real-time adjustment is carried out, and an optimal scheme under the condition of current data is obtained through calculation every fixed time interval; for the current scheme, judging whether the new scheme is more excellent than the current scheme, and if so, replacing the current feasible scheme.
According to the scheme for automatically predicting the ticket selling quantity according to the historical data and the actual data, the prediction scheme is realized by using various actual data and various algorithms; according to the loading result and ticket selling quantity forecast, real-time automatic adjustment of ticket selling quantity is carried out; automatically carrying out load calculation according to historical data and current data, wherein the load uses a simulation estimation and accurate calculation (heuristic search A); and a method for forecasting the load scheme according to the historical load result and the number of the current vehicles in the port.
In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, or the like. Volatile memory can include Random access memory (Random AccessMemory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory (StaticRandomAccessMemory, SRAM) or dynamic random access memory (DynamicRandomAccessMemory, DRAM), among others.
It is to be understood that the foregoing is only illustrative of the principles of the application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (5)
1. A method for predicting ticket sales quantity in real time, comprising the steps of:
input data acquisition is carried out on a plurality of data influencing ticketing;
preprocessing the acquired input data;
predicting the traffic flow of the person according to the processed data;
the specific steps of ticket selling quantity prediction according to the obtained people and vehicle flow prediction result include: according to the predicted result of the traffic flow of people and vehicles, predicting and calculating the ticket selling quantity: according to the actual loading quantity of the ship in history, calculating the actual loading quantity of the ship as the meter number of the lane lines, and obtaining the actual loading meter number of the ship; according to the training model result, using actual data to predict the flow of people and vehicles needing to be loaded by the ship; determining the proportion of each type according to the predicted data, distributing the number of the loadable lane lines in meters, and finally converting the number into the number of each type of vehicles; the converted quantity of various vehicles is used as a default ticketing quantity result;
the method comprises the specific steps of calculating the load quantity, wherein the specific steps of calculating the load quantity are as follows: collecting historical data in batches to obtain the highest historical load quantity; estimating the number of lanes loaded at the time by using the average lane line occupation length of various vehicles loaded at the time according to the highest 15% loading number; according to the obtained number of the loading lanes, calculating and obtaining the maximum practical number of each lane line in the plurality of lane lines; according to the history data, learning, and counting the proportion of various vehicles according to the current ticketing and actual port-keeping conditions; dividing the length of the lane lines according to the proportion of various vehicles, and equally dividing according to the counted length of the lane lines; after the predicted number of various vehicles is obtained, the number of various on-site vehicles is distributed in real time according to the number of the on-site vehicles, the remaining length of the shortest lane line is solved by using a bipartite answer, and the specific position of each vehicle on each lane line is obtained;
according to the influence of various factors in the ticketing process, selecting a real-time adjustment scheme for predicting the ticketing quantity: according to the number of the sold tickets, real-time adjustment is carried out: firstly, calling a ticket selling model prediction result to obtain the proportion of various tickets to be sold; according to the number of the sold tickets, converting the number of the sold tickets into the length of the lane line of the ship, and obtaining the residual ticket-selling space of the ship; and finally, determining the remaining ticketing quantity by using the proportional value obtained by the ticketing quantity prediction method according to the remaining ticketing space.
2. The method according to claim 1, wherein the data of the plurality of ticket influencing sales is input into the data collection step, and the collected data information includes a plurality of historical ticket selling data, weather information and holiday information, wherein the historical ticket selling data is data within one year.
3. The method for predicting the number of ticketing processed in real time as recited in claim 2 wherein said historical ticketing data is used to smooth possible arrival specific data;
and predicting the traffic flow of the person according to the processed data by adopting an autoregressive moving average model.
4. A method of real-time adjusted ticketing quantity prediction as defined in claim 3, wherein,
the specific steps for predicting the traffic flow of the person and the vehicle according to the processed data comprise:
constructing an autoregressive moving average model based on the processed data;
the processed data are divided into a training set and a testing set according to time sequence;
training the built autoregressive moving average model by adopting a training set;
and predicting the test set by using the trained autoregressive moving average model.
5. The method of claim 1, wherein the real-time adjustment scheme comprises: the time period is used for adjusting one or more of different ticketing quantity, recalculating the ticketing quantity every fixed time interval, selecting the time interval to recalculate the ticketing quantity, calculating the ticketing quantity according to the ticketing quantity and recalculating the ticketing quantity according to the loaded quantity.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832860A (en) * | 2017-11-06 | 2018-03-23 | 中交第二航务工程勘察设计院有限公司 | A kind of passenger-cargo rolling dress harbour wisdom production operation method based on BIM technology |
| CN107909170A (en) * | 2017-11-06 | 2018-04-13 | 中交第二航务工程勘察设计院有限公司 | A kind of passenger-cargo rolling dress harbour of intelligence based on BIM technology |
| CN108596539A (en) * | 2018-04-17 | 2018-09-28 | 武汉理工大学 | A kind of passenger-cargo roll-on-roll-off ship intelligent stowage method |
| CN111080504A (en) * | 2019-12-31 | 2020-04-28 | 大连大学 | Passenger transport ferry production intelligent operation management system |
| CN111523560A (en) * | 2020-03-18 | 2020-08-11 | 第四范式（北京）技术有限公司 | Training method, prediction method, device and system for number prediction model of arriving trucks |
| CN112184060A (en) * | 2020-10-21 | 2021-01-05 | 武汉理工大学 | Passenger and cargo roll-on-roll-off waiting ferry parking space arrangement-ship stowage intelligent combined scheduling method |
| CN114220184A (en) * | 2021-12-09 | 2022-03-22 | 江苏镇扬汽渡有限公司 | Intelligent checking and recording system for ferry boat |
| CN114898476A (en) * | 2022-04-21 | 2022-08-12 | 南京亿儒科技有限公司 | Automobile ferry free flow system |
| CN114970983A (en) * | 2022-05-10 | 2022-08-30 | 武汉理工大学 | Passenger-rolling ship stowage optimization method based on improved LHL algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210248543A1 (en) * | 2020-02-07 | 2021-08-12 | Mastercard International Incorporated | Systems and methods for use in data exchanges related to shipping lines |
-
2022
- 2022-12-13 CN CN202211602767.2A patent/CN115829138B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832860A (en) * | 2017-11-06 | 2018-03-23 | 中交第二航务工程勘察设计院有限公司 | A kind of passenger-cargo rolling dress harbour wisdom production operation method based on BIM technology |
| CN107909170A (en) * | 2017-11-06 | 2018-04-13 | 中交第二航务工程勘察设计院有限公司 | A kind of passenger-cargo rolling dress harbour of intelligence based on BIM technology |
| CN108596539A (en) * | 2018-04-17 | 2018-09-28 | 武汉理工大学 | A kind of passenger-cargo roll-on-roll-off ship intelligent stowage method |
| CN111080504A (en) * | 2019-12-31 | 2020-04-28 | 大连大学 | Passenger transport ferry production intelligent operation management system |
| CN111523560A (en) * | 2020-03-18 | 2020-08-11 | 第四范式（北京）技术有限公司 | Training method, prediction method, device and system for number prediction model of arriving trucks |
| CN112184060A (en) * | 2020-10-21 | 2021-01-05 | 武汉理工大学 | Passenger and cargo roll-on-roll-off waiting ferry parking space arrangement-ship stowage intelligent combined scheduling method |
| CN114220184A (en) * | 2021-12-09 | 2022-03-22 | 江苏镇扬汽渡有限公司 | Intelligent checking and recording system for ferry boat |
| CN114898476A (en) * | 2022-04-21 | 2022-08-12 | 南京亿儒科技有限公司 | Automobile ferry free flow system |
| CN114970983A (en) * | 2022-05-10 | 2022-08-30 | 武汉理工大学 | Passenger-rolling ship stowage optimization method based on improved LHL algorithm |
Non-Patent Citations (4)
| Title |
|---|
| 一个滚装客船网络售票及优化配载***;王忠煜,张俊;交通与计算机(第S1期);全文 * |
| 客货滚装港口数字孪生智慧运作模式;魏世桥;王东魁;张煜;马少康;;港口装卸(第01期);全文 * |
| 滚装船舶车辆优化配载***的研究与实现;张俊, 王忠煜, 曹志英;交通与计算机(第S1期);全文 * |
| 论客滚运输经营中大型船舶的配载与管理;张金秀;;港口经济(第05期);全文 * |
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